Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations77607
Missing cells531323
Missing cells (%)36.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.6 MiB
Average record size in memory413.5 B

Variable types

Text4
DateTime5
Categorical4
Numeric6

Alerts

nbnouveaux has constant value "0.0"Constant
pctg has constant value "0.0"Constant
ipmoy has 9533 (12.3%) missing valuesMissing
skus has 67388 (86.8%) missing valuesMissing
op has 67388 (86.8%) missing valuesMissing
date_debut has 67388 (86.8%) missing valuesMissing
date_fin has 67388 (86.8%) missing valuesMissing
display_date has 7891 (10.2%) missing valuesMissing
sku has 7891 (10.2%) missing valuesMissing
impression_count has 7891 (10.2%) missing valuesMissing
acquisition_cost has 7891 (10.2%) missing valuesMissing
tracking_day has 73556 (94.8%) missing valuesMissing
product_id_1 has 73556 (94.8%) missing valuesMissing
count has 73556 (94.8%) missing valuesMissing
clients has 57393 (74.0%) zerosZeros
acquisition_cost has 24640 (31.7%) zerosZeros

Reproduction

Analysis started2024-08-19 16:05:30.582170
Analysis finished2024-08-19 16:05:48.923676
Duration18.34 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

product_id
Text

Distinct1394
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
2024-08-19T16:05:49.203526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length16
Median length15
Mean length13.199235
Min length5

Characters and Unicode

Total characters1024353
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row003411COBURG
2nd row003411COBURG
3rd row003411COBURG
4th row003411COBURG
5th row003411COBURG
ValueCountFrequency (%)
all8718444082217 122
 
0.2%
5452860 122
 
0.2%
03k5386486 122
 
0.2%
060326a100 122
 
0.2%
03k58593 122
 
0.2%
auc0082686390408 122
 
0.2%
5035223125167 122
 
0.2%
air3335016935822 122
 
0.2%
183990101 122
 
0.2%
439030kt110 122
 
0.2%
Other values (1384) 76387
98.4%
2024-08-19T16:05:49.975394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 117337
 
11.5%
1 74750
 
7.3%
3 73041
 
7.1%
2 70513
 
6.9%
4 68912
 
6.7%
5 63520
 
6.2%
6 59399
 
5.8%
8 56985
 
5.6%
9 53506
 
5.2%
7 48693
 
4.8%
Other values (26) 337697
33.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 686656
67.0%
Uppercase Letter 337697
33.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 40350
 
11.9%
S 26460
 
7.8%
C 24485
 
7.3%
O 23068
 
6.8%
E 21272
 
6.3%
B 20669
 
6.1%
T 18171
 
5.4%
R 17846
 
5.3%
I 16849
 
5.0%
L 15527
 
4.6%
Other values (16) 113000
33.5%
Decimal Number
ValueCountFrequency (%)
0 117337
17.1%
1 74750
10.9%
3 73041
10.6%
2 70513
10.3%
4 68912
10.0%
5 63520
9.3%
6 59399
8.7%
8 56985
8.3%
9 53506
7.8%
7 48693
7.1%

Most occurring scripts

ValueCountFrequency (%)
Common 686656
67.0%
Latin 337697
33.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 40350
 
11.9%
S 26460
 
7.8%
C 24485
 
7.3%
O 23068
 
6.8%
E 21272
 
6.3%
B 20669
 
6.1%
T 18171
 
5.4%
R 17846
 
5.3%
I 16849
 
5.0%
L 15527
 
4.6%
Other values (16) 113000
33.5%
Common
ValueCountFrequency (%)
0 117337
17.1%
1 74750
10.9%
3 73041
10.6%
2 70513
10.3%
4 68912
10.0%
5 63520
9.3%
6 59399
8.7%
8 56985
8.3%
9 53506
7.8%
7 48693
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1024353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 117337
 
11.5%
1 74750
 
7.3%
3 73041
 
7.1%
2 70513
 
6.9%
4 68912
 
6.7%
5 63520
 
6.2%
6 59399
 
5.8%
8 56985
 
5.6%
9 53506
 
5.2%
7 48693
 
4.8%
Other values (26) 337697
33.0%

datej
Date

Distinct61
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size606.4 KiB
Minimum2024-02-17 00:00:00
Maximum2024-04-17 00:00:00
2024-08-19T16:05:50.319517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:50.643931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.4 MiB
1P
58902 
3P
18704 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters155212
Distinct characters3
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1P
2nd row1P
3rd row1P
4th row1P
5th row1P

Common Values

ValueCountFrequency (%)
1P 58902
75.9%
3P 18704
 
24.1%
(Missing) 1
 
< 0.1%

Length

2024-08-19T16:05:50.944884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-19T16:05:51.211664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1p 58902
75.9%
3p 18704
 
24.1%

Most occurring characters

ValueCountFrequency (%)
P 77606
50.0%
1 58902
37.9%
3 18704
 
12.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 77606
50.0%
Decimal Number 77606
50.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 58902
75.9%
3 18704
 
24.1%
Uppercase Letter
ValueCountFrequency (%)
P 77606
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 77606
50.0%
Common 77606
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 58902
75.9%
3 18704
 
24.1%
Latin
ValueCountFrequency (%)
P 77606
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 155212
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 77606
50.0%
1 58902
37.9%
3 18704
 
12.1%

prixmoy
Real number (ℝ)

Distinct1795
Distinct (%)2.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean21564.653
Minimum0
Maximum1019999
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size606.4 KiB
2024-08-19T16:05:51.486630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1118
Q15699
median13990
Q328999
95-th percentile63998
Maximum1019999
Range1019999
Interquartile range (IQR)23300

Descriptive statistics

Standard deviation30431.745
Coefficient of variation (CV)1.4111864
Kurtosis423.91036
Mean21564.653
Median Absolute Deviation (MAD)9791
Skewness14.393137
Sum1.6735465 × 109
Variance9.2609109 × 108
MonotonicityNot monotonic
2024-08-19T16:05:51.820450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19999 1674
 
2.2%
9999 1665
 
2.1%
29999 1299
 
1.7%
14999 1288
 
1.7%
12999 1255
 
1.6%
15999 1226
 
1.6%
18999 1194
 
1.5%
7999 1083
 
1.4%
6999 1081
 
1.4%
39999 1079
 
1.4%
Other values (1785) 64762
83.4%
ValueCountFrequency (%)
0 4
 
< 0.1%
58 1
 
< 0.1%
64 1
 
< 0.1%
65 6
 
< 0.1%
72 7
 
< 0.1%
79 61
0.1%
91 7
 
< 0.1%
124 15
 
< 0.1%
129 12
 
< 0.1%
144 7
 
< 0.1%
ValueCountFrequency (%)
1019999 5
 
< 0.1%
999900 25
< 0.1%
359900 13
 
< 0.1%
289999 1
 
< 0.1%
273401 23
< 0.1%
266902 23
< 0.1%
169999 39
0.1%
169900 2
 
< 0.1%
161999 26
< 0.1%
159999 50
0.1%

ipmoy
Real number (ℝ)

MISSING 

Distinct17316
Distinct (%)25.4%
Missing9533
Missing (%)12.3%
Infinite0
Infinite (%)0.0%
Mean95.709294
Minimum30.271
Maximum295.681
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size606.4 KiB
2024-08-19T16:05:52.167255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum30.271
5-th percentile61.71385
Q185.268013
median95.245
Q3101.53965
95-th percentile132.93
Maximum295.681
Range265.41
Interquartile range (IQR)16.271633

Descriptive statistics

Standard deviation23.279244
Coefficient of variation (CV)0.24322866
Kurtosis11.208595
Mean95.709294
Median Absolute Deviation (MAD)7.776
Skewness1.8663255
Sum6515314.5
Variance541.92318
MonotonicityNot monotonic
2024-08-19T16:05:52.502312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 7801
 
10.1%
101.9960798 298
 
0.4%
102.4951248 255
 
0.3%
102.3763036 178
 
0.2%
99.99777773 175
 
0.2%
115.998 165
 
0.2%
101.6633888 164
 
0.2%
105.496 146
 
0.2%
93.33244433 140
 
0.2%
66.577 128
 
0.2%
Other values (17306) 58624
75.5%
(Missing) 9533
 
12.3%
ValueCountFrequency (%)
30.271 1
 
< 0.1%
30.29827316 2
 
< 0.1%
30.33001571 1
 
< 0.1%
30.366 1
 
< 0.1%
30.408 1
 
< 0.1%
30.46398046 3
 
< 0.1%
30.56768559 44
0.1%
30.568 45
0.1%
30.682 1
 
< 0.1%
30.86521739 1
 
< 0.1%
ValueCountFrequency (%)
295.6810036 2
 
< 0.1%
295.3831948 12
< 0.1%
294.8980583 1
 
< 0.1%
293.363033 11
< 0.1%
292.4359969 2
 
< 0.1%
288.6551623 2
 
< 0.1%
287.826 1
 
< 0.1%
286.1230329 5
< 0.1%
284.5145715 1
 
< 0.1%
282.1224121 1
 
< 0.1%

nbnouveaux
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.4 MiB
0.0
77606 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters232818
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 77606
> 99.9%
(Missing) 1
 
< 0.1%

Length

2024-08-19T16:05:52.792219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-19T16:05:53.746349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 77606
100.0%

Most occurring characters

ValueCountFrequency (%)
0 155212
66.7%
. 77606
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 155212
66.7%
Other Punctuation 77606
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 155212
100.0%
Other Punctuation
ValueCountFrequency (%)
. 77606
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 232818
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 155212
66.7%
. 77606
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 232818
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 155212
66.7%
. 77606
33.3%

clients
Real number (ℝ)

ZEROS 

Distinct68
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4.3209288
Minimum0
Maximum1080
Zeros57393
Zeros (%)74.0%
Negative0
Negative (%)0.0%
Memory size606.4 KiB
2024-08-19T16:05:53.993818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile20
Maximum1080
Range1080
Interquartile range (IQR)5

Descriptive statistics

Standard deviation15.298277
Coefficient of variation (CV)3.5405066
Kurtosis565.79176
Mean4.3209288
Median Absolute Deviation (MAD)0
Skewness15.38942
Sum335330
Variance234.03728
MonotonicityNot monotonic
2024-08-19T16:05:54.326013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 57393
74.0%
5 8824
 
11.4%
10 4281
 
5.5%
15 2016
 
2.6%
20 1428
 
1.8%
25 739
 
1.0%
30 685
 
0.9%
40 390
 
0.5%
35 356
 
0.5%
45 231
 
0.3%
Other values (58) 1263
 
1.6%
ValueCountFrequency (%)
0 57393
74.0%
5 8824
 
11.4%
10 4281
 
5.5%
15 2016
 
2.6%
20 1428
 
1.8%
25 739
 
1.0%
30 685
 
0.9%
35 356
 
0.5%
40 390
 
0.5%
45 231
 
0.3%
ValueCountFrequency (%)
1080 1
< 0.1%
645 1
< 0.1%
600 2
< 0.1%
525 1
< 0.1%
500 1
< 0.1%
480 1
< 0.1%
475 1
< 0.1%
460 1
< 0.1%
410 2
< 0.1%
405 1
< 0.1%

pctg
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.4 MiB
0.0
77606 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters232818
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 77606
> 99.9%
(Missing) 1
 
< 0.1%

Length

2024-08-19T16:05:54.616202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-19T16:05:54.868798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 77606
100.0%

Most occurring characters

ValueCountFrequency (%)
0 155212
66.7%
. 77606
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 155212
66.7%
Other Punctuation 77606
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 155212
100.0%
Other Punctuation
ValueCountFrequency (%)
. 77606
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 232818
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 155212
66.7%
. 77606
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 232818
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 155212
66.7%
. 77606
33.3%

skus
Text

MISSING 

Distinct1122
Distinct (%)11.0%
Missing67388
Missing (%)86.8%
Memory size2.7 MiB
2024-08-19T16:05:55.244203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length16
Median length15
Mean length13.309815
Min length5

Characters and Unicode

Total characters136013
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)0.3%

Sample

1st row03K14743
2nd row03K14743
3rd row03K14743
4th row03K14743
5th row03K5623286
ValueCountFrequency (%)
0603b07000 36
 
0.4%
4357760 34
 
0.3%
5133004451 34
 
0.3%
10811300 28
 
0.3%
13877 26
 
0.3%
9s717l541854 26
 
0.3%
ametzrose 26
 
0.3%
2282240 26
 
0.3%
auc0082686390408 26
 
0.3%
202b08070 26
 
0.3%
Other values (1112) 9931
97.2%
2024-08-19T16:05:55.971680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15425
 
11.3%
2 9341
 
6.9%
1 9235
 
6.8%
4 9069
 
6.7%
3 8536
 
6.3%
5 7932
 
5.8%
6 7924
 
5.8%
8 7310
 
5.4%
9 7235
 
5.3%
7 6766
 
5.0%
Other values (26) 47240
34.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 88773
65.3%
Uppercase Letter 47240
34.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 5490
 
11.6%
C 3979
 
8.4%
S 3730
 
7.9%
O 3514
 
7.4%
B 3210
 
6.8%
E 2899
 
6.1%
R 2548
 
5.4%
T 2083
 
4.4%
L 2006
 
4.2%
M 1976
 
4.2%
Other values (16) 15805
33.5%
Decimal Number
ValueCountFrequency (%)
0 15425
17.4%
2 9341
10.5%
1 9235
10.4%
4 9069
10.2%
3 8536
9.6%
5 7932
8.9%
6 7924
8.9%
8 7310
8.2%
9 7235
8.2%
7 6766
7.6%

Most occurring scripts

ValueCountFrequency (%)
Common 88773
65.3%
Latin 47240
34.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 5490
 
11.6%
C 3979
 
8.4%
S 3730
 
7.9%
O 3514
 
7.4%
B 3210
 
6.8%
E 2899
 
6.1%
R 2548
 
5.4%
T 2083
 
4.4%
L 2006
 
4.2%
M 1976
 
4.2%
Other values (16) 15805
33.5%
Common
ValueCountFrequency (%)
0 15425
17.4%
2 9341
10.5%
1 9235
10.4%
4 9069
10.2%
3 8536
9.6%
5 7932
8.9%
6 7924
8.9%
8 7310
8.2%
9 7235
8.2%
7 6766
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 136013
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15425
 
11.3%
2 9341
 
6.9%
1 9235
 
6.8%
4 9069
 
6.7%
3 8536
 
6.3%
5 7932
 
5.8%
6 7924
 
5.8%
8 7310
 
5.4%
9 7235
 
5.3%
7 6766
 
5.0%
Other values (26) 47240
34.7%

op
Categorical

MISSING 

Distinct6
Distinct (%)0.1%
Missing67388
Missing (%)86.8%
Memory size4.7 MiB
100% remboursée
7088 
WE Discount rénovation​
1958 
WE Discount PEM
 
586
Les imbattables 2
 
301
WE Discount GEM
 
273

Length

Max length23
Median length15
Mean length16.58538
Min length10

Characters and Unicode

Total characters169486
Distinct characters30
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWE Discount rénovation​
2nd rowWE Discount rénovation​
3rd rowWE Discount rénovation​
4th rowWE Discount rénovation​
5th row100% remboursée

Common Values

ValueCountFrequency (%)
100% remboursée 7088
 
9.1%
WE Discount rénovation​ 1958
 
2.5%
WE Discount PEM 586
 
0.8%
Les imbattables 2 301
 
0.4%
WE Discount GEM 273
 
0.4%
Imbattable 13
 
< 0.1%
(Missing) 67388
86.8%

Length

2024-08-19T16:05:56.323136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-19T16:05:56.625958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
100 7088
30.1%
remboursée 7088
30.1%
we 2817
 
12.0%
discount 2817
 
12.0%
rénovation​ 1958
 
8.3%
pem 586
 
2.5%
les 301
 
1.3%
imbattables 301
 
1.3%
2 301
 
1.3%
gem 273
 
1.2%

Most occurring characters

ValueCountFrequency (%)
r 16134
 
9.5%
e 14791
 
8.7%
0 14176
 
8.4%
o 13821
 
8.2%
11366
 
6.7%
s 10507
 
6.2%
u 9905
 
5.8%
é 9046
 
5.3%
b 7716
 
4.6%
m 7402
 
4.4%
Other values (20) 54622
32.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 114209
67.4%
Decimal Number 21565
 
12.7%
Space Separator 13324
 
7.9%
Uppercase Letter 11342
 
6.7%
Other Punctuation 7088
 
4.2%
Format 1958
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 16134
14.1%
e 14791
13.0%
o 13821
12.1%
s 10507
9.2%
u 9905
8.7%
é 9046
7.9%
b 7716
6.8%
m 7402
6.5%
n 6733
5.9%
t 5403
 
4.7%
Other values (5) 12751
11.2%
Uppercase Letter
ValueCountFrequency (%)
E 3676
32.4%
D 2817
24.8%
W 2817
24.8%
M 859
 
7.6%
P 586
 
5.2%
L 301
 
2.7%
G 273
 
2.4%
I 13
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 14176
65.7%
1 7088
32.9%
2 301
 
1.4%
Space Separator
ValueCountFrequency (%)
11366
85.3%
  1958
 
14.7%
Other Punctuation
ValueCountFrequency (%)
% 7088
100.0%
Format
ValueCountFrequency (%)
​ 1958
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 125551
74.1%
Common 43935
 
25.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 16134
12.9%
e 14791
11.8%
o 13821
11.0%
s 10507
8.4%
u 9905
7.9%
é 9046
 
7.2%
b 7716
 
6.1%
m 7402
 
5.9%
n 6733
 
5.4%
t 5403
 
4.3%
Other values (13) 24093
19.2%
Common
ValueCountFrequency (%)
0 14176
32.3%
11366
25.9%
1 7088
16.1%
% 7088
16.1%
  1958
 
4.5%
​ 1958
 
4.5%
2 301
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 156524
92.4%
None 11004
 
6.5%
Punctuation 1958
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 16134
 
10.3%
e 14791
 
9.4%
0 14176
 
9.1%
o 13821
 
8.8%
11366
 
7.3%
s 10507
 
6.7%
u 9905
 
6.3%
b 7716
 
4.9%
m 7402
 
4.7%
1 7088
 
4.5%
Other values (17) 43618
27.9%
None
ValueCountFrequency (%)
é 9046
82.2%
  1958
 
17.8%
Punctuation
ValueCountFrequency (%)
​ 1958
100.0%

date_debut
Date

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing67388
Missing (%)86.8%
Memory size606.4 KiB
Minimum2024-02-07 00:00:00
Maximum2024-04-04 00:00:00
2024-08-19T16:05:56.848119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:57.069177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)

date_fin
Date

MISSING 

Distinct6
Distinct (%)0.1%
Missing67388
Missing (%)86.8%
Memory size606.4 KiB
Minimum2024-02-19 00:00:00
Maximum2024-04-30 00:00:00
2024-08-19T16:05:57.308114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:57.536696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)

display_date
Date

MISSING 

Distinct61
Distinct (%)0.1%
Missing7891
Missing (%)10.2%
Memory size606.4 KiB
Minimum2024-02-17 00:00:00
Maximum2024-04-17 00:00:00
2024-08-19T16:05:57.800165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:58.136660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

sku
Text

MISSING 

Distinct1327
Distinct (%)1.9%
Missing7891
Missing (%)10.2%
Memory size4.9 MiB
2024-08-19T16:05:58.741605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length16
Median length15
Mean length13.042458
Min length5

Characters and Unicode

Total characters909268
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row003411COBURG
2nd row003411COBURG
3rd row003411COBURG
4th row003411COBURG
5th row003411COBURG
ValueCountFrequency (%)
all8718444082217 122
 
0.2%
287b06009 122
 
0.2%
5452860 122
 
0.2%
060326a100 122
 
0.2%
03k5386486 122
 
0.2%
03k58593 122
 
0.2%
4020628687489 122
 
0.2%
202b08070 122
 
0.2%
ametzrose 122
 
0.2%
5035223125167 122
 
0.2%
Other values (1317) 68496
98.3%
2024-08-19T16:05:59.807164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 105467
 
11.6%
1 67216
 
7.4%
3 64773
 
7.1%
2 62607
 
6.9%
4 60446
 
6.6%
5 54443
 
6.0%
6 50641
 
5.6%
8 48147
 
5.3%
9 45390
 
5.0%
7 43763
 
4.8%
Other values (26) 306375
33.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 602893
66.3%
Uppercase Letter 306375
33.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 34826
 
11.4%
S 23742
 
7.7%
C 22076
 
7.2%
O 20938
 
6.8%
B 19515
 
6.4%
E 18835
 
6.1%
T 17026
 
5.6%
R 16651
 
5.4%
I 15506
 
5.1%
L 13922
 
4.5%
Other values (16) 103338
33.7%
Decimal Number
ValueCountFrequency (%)
0 105467
17.5%
1 67216
11.1%
3 64773
10.7%
2 62607
10.4%
4 60446
10.0%
5 54443
9.0%
6 50641
8.4%
8 48147
8.0%
9 45390
7.5%
7 43763
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common 602893
66.3%
Latin 306375
33.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 34826
 
11.4%
S 23742
 
7.7%
C 22076
 
7.2%
O 20938
 
6.8%
B 19515
 
6.4%
E 18835
 
6.1%
T 17026
 
5.6%
R 16651
 
5.4%
I 15506
 
5.1%
L 13922
 
4.5%
Other values (16) 103338
33.7%
Common
ValueCountFrequency (%)
0 105467
17.5%
1 67216
11.1%
3 64773
10.7%
2 62607
10.4%
4 60446
10.0%
5 54443
9.0%
6 50641
8.4%
8 48147
8.0%
9 45390
7.5%
7 43763
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 909268
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 105467
 
11.6%
1 67216
 
7.4%
3 64773
 
7.1%
2 62607
 
6.9%
4 60446
 
6.6%
5 54443
 
6.0%
6 50641
 
5.6%
8 48147
 
5.3%
9 45390
 
5.0%
7 43763
 
4.8%
Other values (26) 306375
33.7%

impression_count
Real number (ℝ)

MISSING 

Distinct6685
Distinct (%)9.6%
Missing7891
Missing (%)10.2%
Infinite0
Infinite (%)0.0%
Mean5262.171
Minimum5
Maximum368535
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size606.4 KiB
2024-08-19T16:06:00.382516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile20
Q1190
median920
Q34085
95-th percentile25010
Maximum368535
Range368530
Interquartile range (IQR)3895

Descriptive statistics

Standard deviation13755.55
Coefficient of variation (CV)2.6140447
Kurtosis110.2735
Mean5262.171
Median Absolute Deviation (MAD)870
Skewness7.9074854
Sum3.6685752 × 108
Variance1.8921517 × 108
MonotonicityNot monotonic
2024-08-19T16:06:00.832623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 1413
 
1.8%
10 1035
 
1.3%
15 910
 
1.2%
20 793
 
1.0%
25 753
 
1.0%
30 694
 
0.9%
40 629
 
0.8%
35 604
 
0.8%
50 533
 
0.7%
55 523
 
0.7%
Other values (6675) 61829
79.7%
(Missing) 7891
 
10.2%
ValueCountFrequency (%)
5 1413
1.8%
10 1035
1.3%
15 910
1.2%
20 793
1.0%
25 753
1.0%
30 694
0.9%
35 604
0.8%
40 629
0.8%
45 509
 
0.7%
50 533
 
0.7%
ValueCountFrequency (%)
368535 1
< 0.1%
360220 1
< 0.1%
351105 1
< 0.1%
343700 1
< 0.1%
331100 1
< 0.1%
323125 1
< 0.1%
322335 1
< 0.1%
320995 1
< 0.1%
319860 1
< 0.1%
313610 1
< 0.1%

acquisition_cost
Real number (ℝ)

MISSING  ZEROS 

Distinct2602
Distinct (%)3.7%
Missing7891
Missing (%)10.2%
Infinite0
Infinite (%)0.0%
Mean10.089033
Minimum0
Maximum2617.3
Zeros24640
Zeros (%)31.7%
Negative0
Negative (%)0.0%
Memory size606.4 KiB
2024-08-19T16:06:01.412454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.05
Q36.85
95-th percentile47.25
Maximum2617.3
Range2617.3
Interquartile range (IQR)6.85

Descriptive statistics

Standard deviation33.130443
Coefficient of variation (CV)3.2838074
Kurtosis707.6317
Mean10.089033
Median Absolute Deviation (MAD)1.05
Skewness15.874566
Sum703367.05
Variance1097.6262
MonotonicityNot monotonic
2024-08-19T16:06:01.906771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24640
31.7%
0.4 689
 
0.9%
0.3 664
 
0.9%
0.45 646
 
0.8%
0.5 633
 
0.8%
0.35 622
 
0.8%
0.6 578
 
0.7%
0.55 556
 
0.7%
0.25 538
 
0.7%
0.65 530
 
0.7%
Other values (2592) 39620
51.1%
(Missing) 7891
 
10.2%
ValueCountFrequency (%)
0 24640
31.7%
0.05 136
 
0.2%
0.1 255
 
0.3%
0.15 365
 
0.5%
0.2 515
 
0.7%
0.25 538
 
0.7%
0.3 664
 
0.9%
0.35 622
 
0.8%
0.4 689
 
0.9%
0.45 646
 
0.8%
ValueCountFrequency (%)
2617.3 1
< 0.1%
1320.9 1
< 0.1%
1119.1 1
< 0.1%
955.55 1
< 0.1%
884.3 1
< 0.1%
883.1 1
< 0.1%
781 1
< 0.1%
765.6 1
< 0.1%
743.55 1
< 0.1%
734.55 1
< 0.1%

tracking_day
Date

MISSING 

Distinct61
Distinct (%)1.5%
Missing73556
Missing (%)94.8%
Memory size606.4 KiB
Minimum2024-02-17 00:00:00
Maximum2024-04-17 00:00:00
2024-08-19T16:06:02.285602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:06:02.641907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

product_id_1
Text

MISSING 

Distinct620
Distinct (%)15.3%
Missing73556
Missing (%)94.8%
Memory size2.5 MiB
2024-08-19T16:06:02.990670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length16
Median length15
Mean length12.768946
Min length5

Characters and Unicode

Total characters51727
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique149 ?
Unique (%)3.7%

Sample

1st row003411COBURG
2nd row016642F
3rd row0191ENFI
4th row0191ENFI
5th row0191ENFI
ValueCountFrequency (%)
4046664069478 83
 
2.0%
5904804905 75
 
1.9%
30035741 69
 
1.7%
287z18002 65
 
1.6%
9s716r821884 61
 
1.5%
sam8806094268164 50
 
1.2%
art3508510069151 46
 
1.1%
82k202apfr 41
 
1.0%
alp8008984864795 39
 
1.0%
6580a2pt 36
 
0.9%
Other values (610) 3486
86.1%
2024-08-19T16:06:03.690463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6093
 
11.8%
2 3853
 
7.4%
1 3745
 
7.2%
4 3656
 
7.1%
6 3243
 
6.3%
8 3130
 
6.1%
3 2919
 
5.6%
5 2710
 
5.2%
7 2450
 
4.7%
9 2313
 
4.5%
Other values (26) 17615
34.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34112
65.9%
Uppercase Letter 17615
34.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1884
 
10.7%
S 1676
 
9.5%
E 1123
 
6.4%
I 1107
 
6.3%
C 1021
 
5.8%
B 1008
 
5.7%
R 983
 
5.6%
O 860
 
4.9%
T 858
 
4.9%
L 829
 
4.7%
Other values (16) 6266
35.6%
Decimal Number
ValueCountFrequency (%)
0 6093
17.9%
2 3853
11.3%
1 3745
11.0%
4 3656
10.7%
6 3243
9.5%
8 3130
9.2%
3 2919
8.6%
5 2710
7.9%
7 2450
7.2%
9 2313
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 34112
65.9%
Latin 17615
34.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1884
 
10.7%
S 1676
 
9.5%
E 1123
 
6.4%
I 1107
 
6.3%
C 1021
 
5.8%
B 1008
 
5.7%
R 983
 
5.6%
O 860
 
4.9%
T 858
 
4.9%
L 829
 
4.7%
Other values (16) 6266
35.6%
Common
ValueCountFrequency (%)
0 6093
17.9%
2 3853
11.3%
1 3745
11.0%
4 3656
10.7%
6 3243
9.5%
8 3130
9.2%
3 2919
8.6%
5 2710
7.9%
7 2450
7.2%
9 2313
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6093
 
11.8%
2 3853
 
7.4%
1 3745
 
7.2%
4 3656
 
7.1%
6 3243
 
6.3%
8 3130
 
6.1%
3 2919
 
5.6%
5 2710
 
5.2%
7 2450
 
4.7%
9 2313
 
4.5%
Other values (26) 17615
34.1%

count
Real number (ℝ)

MISSING 

Distinct268
Distinct (%)6.6%
Missing73556
Missing (%)94.8%
Infinite0
Infinite (%)0.0%
Mean28.262404
Minimum1
Maximum2425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size606.4 KiB
2024-08-19T16:06:04.028811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q36
95-th percentile149.5
Maximum2425
Range2424
Interquartile range (IQR)5

Descriptive statistics

Standard deviation116.17685
Coefficient of variation (CV)4.11065
Kurtosis104.5395
Mean28.262404
Median Absolute Deviation (MAD)1
Skewness8.6143804
Sum114491
Variance13497.061
MonotonicityNot monotonic
2024-08-19T16:06:04.362677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2015
 
2.6%
2 493
 
0.6%
3 227
 
0.3%
4 156
 
0.2%
5 115
 
0.1%
7 62
 
0.1%
6 61
 
0.1%
8 56
 
0.1%
12 41
 
0.1%
9 35
 
< 0.1%
Other values (258) 790
 
1.0%
(Missing) 73556
94.8%
ValueCountFrequency (%)
1 2015
2.6%
2 493
 
0.6%
3 227
 
0.3%
4 156
 
0.2%
5 115
 
0.1%
6 61
 
0.1%
7 62
 
0.1%
8 56
 
0.1%
9 35
 
< 0.1%
10 17
 
< 0.1%
ValueCountFrequency (%)
2425 1
< 0.1%
1773 1
< 0.1%
1716 1
< 0.1%
1403 1
< 0.1%
1292 2
< 0.1%
1243 1
< 0.1%
1235 1
< 0.1%
1177 1
< 0.1%
1141 2
< 0.1%
1117 1
< 0.1%

Interactions

2024-08-19T16:05:43.499977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:34.625457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:36.320188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:37.963630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:39.610352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:41.530603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:43.773498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:34.929609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:36.598723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:38.239404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:39.908967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:42.141361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:44.157128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:35.211607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:36.867905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:38.527934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:40.258378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:42.430961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:44.533530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:35.514019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:37.137385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:38.782092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:40.666502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:42.698095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:44.936524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:35.786078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:37.433310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:39.077383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:40.955094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:42.986995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:45.262381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:36.045067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:37.685367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:39.327043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:41.222099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-19T16:05:43.245953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Missing values

2024-08-19T16:05:45.897202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-19T16:05:47.124520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-19T16:05:48.436202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

product_iddatejtypeprixmoyipmoynbnouveauxclientspctgskusopdate_debutdate_findisplay_dateskuimpression_countacquisition_costtracking_dayproduct_id_1count
0003411COBURG2024-02-171P15999.0102.3763040.015.00.0NaNNaNNaTNaT2024-02-17003411COBURG4650.011.65NaTNaNNaN
1003411COBURG2024-02-191P15999.0102.3763040.05.00.0NaNNaNNaTNaT2024-02-19003411COBURG5025.08.15NaTNaNNaN
2003411COBURG2024-02-201P15999.0102.3763040.05.00.0NaNNaNNaTNaT2024-02-20003411COBURG4625.09.40NaTNaNNaN
3003411COBURG2024-02-211P15999.0102.3763040.00.00.0NaNNaNNaTNaT2024-02-21003411COBURG4765.03.25NaTNaNNaN
4003411COBURG2024-02-221P15999.0102.3763040.05.00.0NaNNaNNaTNaT2024-02-22003411COBURG3395.05.90NaTNaNNaN
5003411COBURG2024-02-231P16999.0107.1384350.00.00.0NaNNaNNaTNaT2024-02-23003411COBURG2155.03.15NaTNaNNaN
6003411COBURG2024-02-241P16999.0107.1384350.05.00.0NaNNaNNaTNaT2024-02-24003411COBURG3225.04.602024-02-24003411COBURG1.0
7003411COBURG2024-02-261P16999.0107.1384350.015.00.0NaNNaNNaTNaT2024-02-26003411COBURG3680.05.95NaTNaNNaN
8003411COBURG2024-02-271P16999.0102.2682850.030.00.0NaNNaNNaTNaT2024-02-27003411COBURG3805.011.15NaTNaNNaN
9003411COBURG2024-02-291P16999.0102.2682850.015.00.0NaNNaNNaTNaT2024-02-29003411COBURG3650.010.10NaTNaNNaN
product_iddatejtypeprixmoyipmoynbnouveauxclientspctgskusopdate_debutdate_findisplay_dateskuimpression_countacquisition_costtracking_dayproduct_id_1count
77597AUC06120688240012024-02-283P20999.069.9990.05.00.0NaNNaNNaTNaT2024-02-28AUC0612068824001190.01.0NaTNaNNaN
77598AUC06120688240012024-02-293P20999.069.9990.00.00.0NaNNaNNaTNaT2024-02-29AUC061206882400130.00.0NaTNaNNaN
77599AUC06120688240012024-03-013P20999.069.9990.00.00.0NaNNaNNaTNaT2024-03-01AUC061206882400175.00.0NaTNaNNaN
77600AUC06120688240012024-03-023P20999.069.9990.00.00.0NaNNaNNaTNaT2024-03-02AUC061206882400165.00.0NaTNaNNaN
77601AUC06120688240012024-03-033P20999.069.9990.00.00.0NaNNaNNaTNaT2024-03-03AUC061206882400140.00.4NaTNaNNaN
77602AUC06120688240012024-03-043P20999.069.9990.00.00.0NaNNaNNaTNaT2024-03-04AUC061206882400150.00.0NaTNaNNaN
77603AUC06120688240012024-03-053P20999.069.9990.00.00.0NaNNaNNaTNaT2024-03-05AUC061206882400130.00.0NaTNaNNaN
77604AUC06120688240012024-03-063P20999.069.9990.00.00.0NaNNaNNaTNaT2024-03-06AUC061206882400135.00.0NaTNaNNaN
77605AUC06120688240012024-03-073P20999.069.9990.00.00.0NaNNaNNaTNaT2024-03-07AUC061206882400140.00.0NaTNaNNaN
77606AUC06120NaTNaNNaNNaNNaNNaNNaNNaNNaNNaTNaTNaTNaNNaNNaNNaTNaNNaN